Implement a Data Analytics Solution with Azure Databricks
At a glance
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By the end of this learning path, you'll have built solid intermediate to advanced skills in both Databricks and Spark on Azure. You're able to ingest, transform, and analyze large-scale datasets using Spark DataFrames, Spark SQL, and PySpark, giving you confidence in working with distributed data processing. Within Databricks, you know how to navigate the workspace, manage clusters, and build and maintain Delta tables.
You'll also be capable of designing and running ETL pipelines, optimizing Delta tables, managing schema changes, and applying data quality rules. In addition, you learn how to orchestrate workloads with Lakeflow Jobs and pipelines, enabling you to move from exploration to automated workflows. Finally, you gain familiarity with governance and security features, including Unity Catalog, Purview integration, and access management, preparing you to operate effectively in production-ready data environments.
Prerequisites
Before starting this learning path, you should already be comfortable with the fundamentals of Python and SQL. This includes being able to write simple Python scripts and work with common data structures, as well as writing SQL queries to filter, join, and aggregate data. A basic understanding of common file formats such as CSV, JSON, or Parquet will also help when working with datasets.
In addition, familiarity with the Azure portal and core services like Azure Storage is important, along with a general awareness of data concepts such as batch versus streaming processing and structured versus unstructured data. While not mandatory, prior exposure to big data frameworks like Spark, and experience working with Jupyter notebooks, can make the transition to Databricks smoother.
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Modules in this learning path
Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
Learn how to perform data analysis using Azure Databricks. Explore various data ingestion methods and how to integrate data from sources like Azure Data Lake and Azure SQL Database. This module guides you through using collaborative notebooks to perform exploratory data analysis (EDA), so you can visualize, manipulate, and examine data to uncover patterns, anomalies, and correlations.
Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.
Delta Lake is a data management solution in Azure Databricks providing features including ACID transactions, schema enforcement, and time travel ensuring data consistency, integrity, and versioning capabilities.
Building Lakeflow Declarative Pipelines enables real-time, scalable, and reliable data processing using Delta Lake's advanced features in Azure Databricks
Deploying workloads with Lakeflow Jobs involves orchestrating and automating complex data processing pipelines, machine learning workflows, and analytics tasks. In this module, you learn how to deploy workloads with Databricks Lakeflow Jobs.